scholarly journals InSite: a computational method for identifying protein-protein interaction binding sites on a proteome-wide scale

2007 ◽  
Vol 8 (9) ◽  
pp. R192 ◽  
Author(s):  
Haidong Wang ◽  
Eran Segal ◽  
Asa Ben-Hur ◽  
Qian-Ru Li ◽  
Marc Vidal ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Binding Sites ◽  
Computational Method ◽  
Protein Protein Interaction ◽  
Wide Scale

scholarly journals Mammalian Protein-Protein Interaction Trap (MAPPIT) Analysis of STAT5, CIS, and SOCS2 Interactions with the Growth Hormone Receptor

2007 ◽  
Vol 21 (11) ◽  
pp. 2821-2831 ◽  
Author(s):  
Isabel Uyttendaele ◽  
Irma Lemmens ◽  
Annick Verhee ◽  
Anne-Sophie De Smet ◽  
Joël Vandekerckhove ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Binding Sites ◽  
Growth Hormone Receptor ◽  
Functional Divergence ◽  
Critical Role ◽  
Sh2 Domain ◽  
Cytokine Signaling ◽  
Protein Protein Interaction ◽  
Interaction Trap ◽  
Mammalian Protein

Abstract Binding of GH to its receptor induces rapid phosphorylation of conserved tyrosine motifs that function as recruitment sites for downstream signaling molecules. Using mammalian protein-protein interaction trap (MAPPIT), a mammalian two-hybrid method, we mapped the binding sites in the GH receptor for signal transducer and activator of transcription 5 (STAT5) a and b and for the negative regulators of cytokine signaling cytokine-inducible Src-homology 2 (SH2)-containing protein (CIS) and suppressor of cytokine signaling 2 (SOCS2). Y534, Y566, and Y627 are the major recruitment sites for STAT5. A non-overlapping recruitment pattern is observed for SOCS2 and CIS with positions Y487 and Y595 as major binding sites, ruling out SOCS-mediated inhibition of STAT5 activation by competition for shared binding sites. More detailed analysis revealed that CIS binding to the Y595, but not to the Y487 motif, depends on both its SH2 domain and the C-terminal part of its SOCS box, with a critical role for the CIS Y253 residue. This functional divergence of the two CIS/SOCS2 recruitment sites is also observed upon substitution of the Y+1 residue by leucine, turning the Y487, but not the Y595 motif into a functional STAT5 recruitment site.

scholarly journals Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Baoman Wang ◽  
Fei Yuan ◽  
Xiangyin Kong ◽  
Lan-Dian Hu ◽  
Yu-Dong Cai
Keyword(s):  
Cell Death ◽  
Candidate Genes ◽  
Protein Interaction ◽  
Permutation Test ◽  
Interaction Network ◽  
Weighted Graph ◽  
Computational Method ◽  
Protein Protein Interaction ◽  
Underlying Mechanisms ◽  
Multicellular Organisms

Apoptosis is the process of programmed cell death (PCD) that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

COMPUTATIONAL METHOD FOR PROTEIN FUNCTION PREDICTION BY CONSTRUCTING PROTEIN INTERACTION NETWORK DICTIONARY

2006 ◽  
Vol 20 (02) ◽  
pp. 285-295 ◽  
Author(s):  
HEE-JEONG JIN ◽  
HWAN-GUE CHO
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Interaction Network ◽  
Computational Method ◽  
Chi Square ◽  
Protein Protein Interaction ◽  
Protein Functions ◽  
Novel Method

In the post-genomic era, predicting protein function is a challenging problem. It is difficult and burdensome work to unravel the functions of a protein by wet experiments only. In this paper, we propose a novel method to predict protein functions by building a "Protein Interaction Network Dictionary (PIND)". This method deduces the protein functions by searching the most similar "words"(an anagram of functions in neighbor proteins on a protein–protein interaction graph) using global alignments. An evaluation of sensitivity and specificity shows that this PIND approach outperforms previous approaches such as Majority Rule and Chi-Square measure, and that it competes with the recently introduced Random Markov Model approach.

scholarly journals DELPHI: accurate deep ensemble model for protein interaction sites prediction

2020 ◽  
Author(s):  
Yiwei Li ◽  
Lucian Ilie
Keyword(s):  
Deep Learning ◽  
Protein Interaction ◽  
Binding Sites ◽  
Data Augmentation ◽  
Fundamental Problem ◽  
Art Programs ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Link Type ◽  
Protein Interaction Sites

AbstractMotivationProteins usually perform their functions by interacting with other proteins, which is why accurately predicting protein-protein interaction (PPI) binding sites is a fundamental problem. Experimental methods are slow and expensive. Therefore, great efforts are being made towards increasing the performance of computational methods.ResultsWe propose DELPHI (DEep Learning Prediction of Highly probable protein Interaction sites), a new sequence-based deep learning suite for PPI binding sites prediction. DELPHI has an ensemble structure with data augmentation and it employs novel features in addition to existing ones. We comprehensively compare DELPHI to nine state-of-the-art programs on five datasets and show that it is more accurate.AvailabilityThe trained model, source code for training, predicting, and data processing are freely available at https://github.com/lucian-ilie/DELPHI. All datasets used in this study can be downloaded at http://www.csd.uwo.ca/~ilie/DELPHI/[email protected]

scholarly journals Jumonji Represses Atrial Natriuretic Factor Gene Expression by Inhibiting Transcriptional Activities of Cardiac Transcription Factors

2004 ◽  
Vol 24 (23) ◽  
pp. 10151-10160 ◽  
Author(s):  
Tae-gyun Kim ◽  
Junqin Chen ◽  
Junich Sadoshima ◽  
Youngsook Lee
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Binding Sites ◽  
Heart Development ◽  
Atrial Natriuretic Factor ◽  
Consensus Sequence ◽  
Cardiac Differentiation ◽  
Protein Protein Interaction ◽  
Natriuretic Factor ◽  
Atrial Natriuretic

ABSTRACT Mice with a homozygous knockout of the jumonji (jmj) gene showed abnormal heart development and defective regulation of cardiac-specific genes, including the atrial natriuretic factor (ANF). ANF is one of the earliest markers of cardiac differentiation and a hallmark for cardiac hypertrophy. Here, we show that JMJ represses ANF gene expression by inhibiting transcriptional activities of Nkx2.5 and GATA4. JMJ represses the Nkx2.5- or GATA4-dependent activation of the reporter genes containing the ANF promoter-enhancer or containing the Nkx2.5 or GATA4-binding consensus sequence. JMJ physically associates with Nkx2.5 and GATA4 in vitro and in vivo as determined by glutathione S-transferase pull-down and immunoprecipitation assays. Using mutational analyses, we mapped the protein-protein interaction domains in JMJ, Nkx2.5, and GATA4. We identified two DNA-binding sites of JMJ in the ANF enhancer by gel mobility shift assays. However, these JMJ-binding sites do not seem to mediate ANF repression by JMJ. Mutational analysis of JMJ indicates that the protein-protein interaction domain of JMJ mediates the repression of ANF gene expression. Therefore, JMJ may play important roles in the down-regulation of ANF gene expression and in heart development.

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